



'''simple docstring'''






from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class 				a__:
    lowercase__					=       42  # [batch_size x 3]
    lowercase__					=       42  # [batch_size x 3]
    lowercase__					=       42  # [batch_size x 3]
    lowercase__					=       42  # [batch_size x 3]
    lowercase__					=       42
    lowercase__					=       42
    lowercase__					=       42
    lowercase__					=       42
    lowercase__					=       42







    def      lowercase_			( self					:    Optional[int]      ):
      assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
      assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
      assert len(self.x.shape      ) == len(self.y.shape      ) == len(self.z.shape      ) == len(self.origin.shape      ) == 2







    def      lowercase_			( self					:    int      ):
      return torch.from_numpy(np.array([self.width, self.height]		,     dtype=np.floataa      )      )







    def      lowercase_			( self					:    Dict      ):
      return torch.from_numpy(np.array([self.x_fov, self.y_fov]		,     dtype=np.floataa      )      )







    def      lowercase_			( self					:    List[str]      ):
      a		:   Tuple	  =	torch.arange(self.height * self.width      )
      a		:   int	  =	torch.stack(
          [
              pixel_indices % self.width,
              torch.div(__snake_case		,     self.width		,     rounding_mode='trunc'      ),
          ]		,     axis=1		,     )
      return coords







    @property
    def      lowercase_			( self					:    str      ):
      a  ,      *a		:   Tuple	  =	self.shape
      a		:   Dict	  =	int(np.prod(__snake_case      )      )

      a		:   Dict	  =	self.get_image_coords()
      a		:   Union[str, Any]	  =	torch.broadcast_to(coords.unsqueeze(0      )		,     [batch_size * inner_batch_size, *coords.shape]      )
      a		:   int	  =	self.get_camera_rays(__snake_case      )

      a		:   Dict	  =	rays.view(__snake_case		,     inner_batch_size * self.height * self.width		,     2		,     3      )

      return rays







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    torch.Tensor      ):
      a  ,      *a  ,      a		:   Optional[Any]	  =	coords.shape
      assert n_coords == 2
      assert batch_size == self.origin.shape[0]

      a		:   Union[str, Any]	  =	coords.view(__snake_case		,     -1		,     2      )

      a		:   Dict	  =	self.resolution()
      a		:   Optional[Any]	  =	self.fov()

      a		:   List[str]	  =	(flat.float() / (res - 1)) * 2 - 1
      a		:   Union[str, Any]	  =	fracs * torch.tan(fov / 2      )

      a		:   List[str]	  =	fracs.view(__snake_case		,     -1		,     2      )
      a		:   Any	  =	(
          self.z.view(__snake_case		,     1		,     3      )
          + self.x.view(__snake_case		,     1		,     3      ) * fracs[:, :, :1]
          + self.y.view(__snake_case		,     1		,     3      ) * fracs[:, :, 1:]
      )
      a		:   Union[str, Any]	  =	directions / directions.norm(dim=-1		,     keepdim=__snake_case      )
      a		:   List[str]	  =	torch.stack(
          [
              torch.broadcast_to(self.origin.view(__snake_case		,     1		,     3      )		,     [batch_size, directions.shape[1], 3]      ),
              directions,
          ]		,     dim=2		,     )
      return rays.view(__snake_case		,     *__snake_case		,     2		,     3      )







    def      lowercase_			( self					:    Any		,     __snake_case					:    int		,     __snake_case					:    int      ):
      assert width * self.height == height * self.width, "The aspect ratio should not change."
      return DifferentiableProjectiveCamera(
          origin=self.origin		,     x=self.x		,     y=self.y		,     z=self.z		,     width=__snake_case		,     height=__snake_case		,     x_fov=self.x_fov		,     y_fov=self.y_fov		,     )
def  lowerCamelCase__			(		_A							):
  a		:   Optional[int]	  =	[]
  a		:   str	  =	[]
  a		:   Optional[int]	  =	[]
  a		:   Dict	  =	[]
  for theta in np.linspace(0					,     2 * np.pi					,     num=20							):
    a		:   Optional[Any]	  =	np.array([np.sin(_A							), np.cos(_A							), -0.5]							)
    z /= np.sqrt(np.sum(z**2							)							)
    a		:   Dict	  =	-z * 4
    a		:   Union[str, Any]	  =	np.array([np.cos(_A							), -np.sin(_A							), 0.0]							)
    a		:   str	  =	np.cross(_A					,     _A							)
    origins.append(_A							)
    xs.append(_A							)
    ys.append(_A							)
    zs.append(_A							)
  return DifferentiableProjectiveCamera(
      origin=torch.from_numpy(np.stack(_A					,     axis=0							)							).float()					,     x=torch.from_numpy(np.stack(_A					,     axis=0							)							).float()					,     y=torch.from_numpy(np.stack(_A					,     axis=0							)							).float()					,     z=torch.from_numpy(np.stack(_A					,     axis=0							)							).float()					,     width=_A					,     height=_A					,     x_fov=0.7					,     y_fov=0.7					,     shape=(1, len(_A							))					,     )




'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
     from PIL import Image

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """naver-clova-ix/donut-base-finetuned-docvqa"""
    lowercase__					=       (
        """This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
        """should be the document containing the information, as well as a `question` that is the question about the """
        """document. It returns a text that contains the answer to the question."""
    )
    lowercase__					=       """document_qa"""
    lowercase__					=       AutoProcessor
    lowercase__					=       VisionEncoderDecoderModel

    lowercase__					=       ["""image""", """text"""]
    lowercase__					=       ["""text"""]







    def __init__( self					:    str		,     *__snake_case					:    Any		,     **__snake_case					:    List[Any]      ):
      if not is_vision_available():
        raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'      )

      super().__init__(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    "Image"		,     __snake_case					:    str      ):
      a		:   Union[str, Any]	  =	'<s_docvqa><s_question>{user_input}</s_question><s_answer>'
      a		:   Any	  =	task_prompt.replace('{user_input}'		,     __snake_case      )
      a		:   Dict	  =	self.pre_processor.tokenizer(
          __snake_case		,     add_special_tokens=__snake_case		,     return_tensors='pt'      ).input_ids
      a		:   Tuple	  =	self.pre_processor(__snake_case		,     return_tensors='pt'      ).pixel_values

      return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}







    def      lowercase_			( self					:    Any		,     __snake_case					:    int      ):
      return self.model.generate(
          inputs['pixel_values'].to(self.device      )		,     decoder_input_ids=inputs['decoder_input_ids'].to(self.device      )		,     max_length=self.model.decoder.config.max_position_embeddings		,     early_stopping=__snake_case		,     pad_token_id=self.pre_processor.tokenizer.pad_token_id		,     eos_token_id=self.pre_processor.tokenizer.eos_token_id		,     use_cache=__snake_case		,     num_beams=1		,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]		,     return_dict_in_generate=__snake_case		,     ).sequences







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    Tuple      ):
      a		:   str	  =	self.pre_processor.batch_decode(__snake_case      )[0]
      a		:   str	  =	sequence.replace(self.pre_processor.tokenizer.eos_token		,     ''      )
      a		:   Union[str, Any]	  =	sequence.replace(self.pre_processor.tokenizer.pad_token		,     ''      )
      a		:   List[str]	  =	re.sub(r'<.*?>'		,     ''		,     __snake_case		,     count=1      ).strip()  # remove first task start token
      a		:   Dict	  =	self.pre_processor.tokenajson(__snake_case      )

      return sequence["answer"]